Search results for: reduced order macro models

Authors: A. M. John-Otumu, M. M. Rahman, M. C. Onuoha, E. P. Ojonugwa

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A Convolutional Neural Network (CNN) model for predicting Lassa fever was built using Python 3.8.0 programming language, alongside Keras 2.2.4 and TensorFlow 2.6.1 libraries as the development environment in order to reduce the current high risk of Lassa fever in West Africa, particularly in Nigeria. The study was prompted by some major flaws in existing conventional laboratory equipment for diagnosing Lassa fever (RT-PCR), as well as flaws in AI-based techniques that have been used for probing and prognosis of Lassa fever based on literature. There were 15,679 blood smear microscopic image datasets collected in total. The proposed model was trained on 70% of the dataset and tested on 30% of the microscopic images in avoid overfitting. A 3x3x3 convolution filter was also used in the proposed system to extract features from microscopic images. The proposed CNN-based model had a recall value of 96%, a precision value of 93%, an F1 score of 95%, and an accuracy of 94% in predicting and accurately classifying the images into clean or infected samples. Based on empirical evidence from the results of the literature consulted, the proposed model outperformed other existing AI-based techniques evaluated. If properly deployed, the model will assist physicians, medical laboratory scientists, and patients in making accurate diagnoses for Lassa fever cases, allowing the mortality rate due to the Lassa fever virus to be reduced through sound decision-making.

Keywords: artificial intelligence, ANN, blood smear, CNN, deep learning, Lassa fever

Procedia PDF Downloads 125

Authors: M. Sadeghi, H. Pezeshk, R. Tusserkani, A. Sharifi Zarchi, A. Malekpour, M. Foroughmand, S. Goliaei, M. Totonchi, N. Ansari–Pour

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The role and relative importance of intrinsic and extrinsic factors in the development of complex diseases such as cancer still remains a controversial issue. Determining the amount of variation explained by these factors needs experimental data and statistical models. These models are nevertheless based on the occurrence and accumulation of random mutational events during stem cell division, thus rendering cancer development a stochastic outcome. We demonstrate that not only individual genome sequencing is uninformative in determining cancer risk, but also assigning a unique genome sequence to any given individual (healthy or affected) is not meaningful. Current whole-genome sequencing approaches are therefore unlikely to realize the promise of personalized medicine. In conclusion, since genome sequence differs from cell to cell and changes over time, it seems that determining the risk factor of complex diseases based on genome sequence is somewhat unrealistic, and therefore, the resulting data are likely to be inherently uninformative.

Keywords: cancer risk, extrinsic factors, genome sequencing, intrinsic factors

Procedia PDF Downloads 275

Authors: Sofia N. Gonçalves, Duarte M. S. Albuquerque, José C. F. Pereira

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Microwave technology has been gaining importance in contributing to decarbonization processes in high energy demand industries. Despite the several numerical models presented in the literature, a proper Verification and Validation exercise is still lacking. This is important and required to evaluate the physical process model accuracy and adequacy. Another issue addresses impedance matching, which is an important mechanism used in microwave experiments to increase electromagnetic efficiency. Such mechanism is not available in current computational tools, thus requiring an external numerical procedure. A numerical model was implemented to study the continuous processing of limestone with microwave heating. This process requires the material to be heated until a certain temperature that will prompt a highly endothermic reaction. Both a 2D and 3D model were built in COMSOL Multiphysics to solve the two-way coupling between Maxwell and Energy equations, along with the coupling between both heat transfer phenomena and limestone endothermic reaction. The 2D model was used to study and evaluate the required numerical procedure, being also a benchmark test, allowing other authors to implement impedance matching procedures. To achieve this goal, a controller built in MATLAB was used to continuously matching the cavity impedance and predicting the required energy for the system, thus successfully avoiding energy inefficiencies. The 3D model reproduces realistic results and therefore supports the main conclusions of this work. Limestone was modeled as a continuous flow under the transport of concentrated species, whose material and kinetics properties were taken from literature. Verification and Validation of the coupled model was taken separately from the chemical kinetic model. The chemical kinetic model was found to correctly describe the chosen kinetic equation by comparing numerical results with experimental data. A solution verification was made for the electromagnetic interface, where second order and fourth order accurate schemes were found for linear and quadratic elements, respectively, with numerical uncertainty lower than 0.03%. Regarding the coupled model, it was demonstrated that the numerical error would diverge for the heat transfer interface with the mapped mesh. Results showed numerical stability for the triangular mesh, and the numerical uncertainty was less than 0.1%. This study evaluated limestone velocity, heat transfer, and load influence on thermal decomposition and overall process efficiency. The velocity and heat transfer coefficient were studied with the 2D model, while different loads of material were studied with the 3D model. Both models demonstrated to be highly unstable when solving non-linear temperature distributions. High velocity flows exhibited propensity to thermal runways, and the thermal efficiency showed the tendency to stabilize for the higher velocities and higher filling ratio. Microwave efficiency denoted an optimal velocity for each heat transfer coefficient, pointing out that electromagnetic efficiency is a consequence of energy distribution uniformity. The 3D results indicated the inefficient development of the electric field for low filling ratios. Thermal efficiencies higher than 90% were found for the higher loads and microwave efficiencies up to 75% were accomplished. The 80% fill ratio was demonstrated to be the optimal load with an associated global efficiency of 70%.

Keywords: multiphysics modeling, microwave heating, verification and validation, endothermic reactions modeling, impedance matching, limestone continuous processing

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Authors: Arya Pirooz

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The effects of different fuels (DME, RME B100, and SME B100) on barrel engines were studied as a general, single dimensional investigation for characterization of these types of engines. A base computational model was created as reference point to be used as a point of comparison with different cases. The models were computed using the commercial computational fluid dynamics program, Diesel-RK. The base model was created using basic dimensions of the PAMAR-3 engine with inline unit injectors. Four fuel cases were considered. Optimized models were also considered for diesel and DME cases with respect to injection duration, fuel, injection timing, exhaust and intake port opening, CR, angular offset. These factors were optimized for highest BMEP, combined PM and NOx emissions, and highest SFC. Results included mechanical efficiency (eta_m), efficiency and power, emission characteristics, combustion characteristics. DME proved to have the highest performing characteristics in relation to diesel and RME fuels for this type of barrel engine.

Keywords: DME, RME, Diesel-RK, characterization, inline unit injector

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Authors: L. Serrano-Lara, J.M Alba-Gómez

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Qubbet el–Hawa Project has been documented archaeological artifacts as 3d models by laser scanning technique since 2015. Currently, research has obtained the right methodology to develop a high accuracy photographic texture for each geometrical 3D model. Furthermore, the right methodology to attach the complete digital surrogate into a 3DPDF document has been obtained; it is used as a catalogue worksheet that brings archaeological data and, at the same time, allows us to obtain precise measurements, volume calculations and cross-section mapping of each scanned artifact. This validated archaeological documentation is the first step for dissemination, application as Qubbet el-Hawa Virtual Museum, and, moreover, multi-sensory experience through 3D print archaeological artifacts. Material culture from four funerary complexes constructed in West Aswan has become physical replicas opening the archaeological research process itself and offering creative possibilities on museology or educational projects. This paper shares a method of acquiring texture for scanning´s output product in order to achieve a 3DPDF archaeological cataloguing, and, on the other hand, to allow the colorfully 3D printing of singular archaeological artifacts. The proposed method has undergone two concrete cases, a polychrome wooden ushabti, and, a cartonnage mask belonging to a lady, bought recovered on intact tomb QH34aa. Both 3D model results have been implemented on three main applications, archaeological 3D catalogue, public dissemination activities, and the 3D artifact model in a bachelor education program. Due to those three already mentioned applications, productive interaction among spectator and three-dimensional artifact have been increased; moreover, functionality as archaeological documentation has been consolidated. Finding the right methodology to assign a specific color to each vector on the geometric 3D model, we had been achieved two essential archaeological applications. Firstly, 3DPDF as a display document for an archaeological catalogue, secondly, the possibility to obtain a colored 3d printed object to be displayed in public exhibitions. Obsolescences 3D models have become updated archaeological documentation of QH43aa tomb cultural material. Therefore, Qubbet el-Hawa Project has been actualized the educational potential of its results thanks to a multi-sensory experience that arose from 3d scanned´s archaeological artifacts.

Keywords: 3D printed, 3D scanner, Middle Kingdom, Qubbet el-Hawa necropolis, virtual archaeology

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Authors: Azza H. El-Medany, Hanan H. Hagar, Jamila H. El-Medany

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Ulcerative colitis (UC) is one of chronic inflammatory diseases primarily affecting colon with unknown etiology. Some researches papers mentioned the possibility of the use of drugs that affect the angiotensin II in reducing the complication of ulcerative colitis. The aim of the present study is to evaluate the potential protective and therapeutic effects of captopril and valsartan on ulcerative colitis induced experimentally in rats using acetic acid. The results were assessed by histological assessment of colonic tissues and measurement of malondialdehyde (MDA), tumor necrosis factor (TNF-α), transforming growth factor (TGF-1B), angiotensin converting enzyme (ACE), reduced glutathione (GSH) and platelet activating factor (PAF) levels in colonic tissues. Oral pre-treatment with captopril or valsartan in a dose of 30 mgkg-1 body weight for 2 weeks before induction of colitis (prophylactic groups) and continuously for 2 weeks after induction (therapeutic groups) significantly reduce MDA, TNF-α, PAF, TGF-1B and ACE levels in colonic tissues as compared to acetic acid control group. Also, a significant increase in GSH level was observed in colonic tissues. Captopril and valsartan attenuated the macroscopic and microscopic colonic damage induced by acetic acid. These results suggest that either captopril or valsartan may be effective as prophylactic or treatment of UC through inhibition of ACE and scavenging effect on oxygen-derived free radicals.

Keywords: captopril, valsartan, angiotensin converting enzyme, reduced glutathione, tumor necrosis factor

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Authors: Moshe Mello, Hilary Rutto, Tumisang Seodigeng

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The adsorptive ability of different carbon materials, tire char (TC), demineralized tire char (DTC), activated tire char (ATC) and Aldrich supplied commercial activated carbon (CAC) was studied for desulfurization of tire pyrolytic oil (TPO). TPO with an initial sulfur content of 7767.7 ppmw was used in this present study. Preparation of ATC was achieved by chemical treatment of raw TC using a potassium hydroxide (KOH) solution and subsequent activation at 800°C in the presence of nitrogen. The thermal behavior of TC, surface microstructure, and the surface functional groups of the carbon materials was investigated using TGA, SEM, and FTIR, respectively. Adsorptive desulfurization of TPO using the carbon materials was performed and they performed in the order of CAC>ATC>DTC>TC. Adsorption kinetics were studied, and pseudo-first order kinetic model displayed a better fit compared to pseudo-second order model. For isotherm studies, the Freundlich isotherm model fitted to the equilibrium data better than the Langmuir isotherm model.

Keywords: ATC, desulfurization, pyrolysis, tire, TPO

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Authors: Subhadip Biswas, Shivendra Maurya, Satish Chandra, Indrajit Ghosh

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Developing speed model is a challenging task particularly under mixed traffic condition where the traffic composition plays a significant role in determining vehicular speed. The present research has been conducted to model individual vehicular speed in the context of mixed traffic on an urban arterial. Traffic speed and volume data have been collected from three midblock arterial road sections in New Delhi. Using the field data, a volume based speed prediction model has been developed adopting the methodology of Artificial Neural Network (ANN). The model developed in this work is capable of estimating speed for individual vehicle category. Validation results show a great deal of agreement between the observed speeds and the predicted values by the model developed. Also, it has been observed that the ANN based model performs better compared to other existing models in terms of accuracy. Finally, the sensitivity analysis has been performed utilizing the model in order to examine the effects of traffic volume and its composition on individual speeds.

Keywords: speed model, artificial neural network, arterial, mixed traffic

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Authors: T. Elrazaz

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This paper examines accrual and real earnings management by publicly listed targets around mergers and acquisitions. Prior literature shows that earnings management around mergers and acquisitions can have a significant economic impact because of the associated wealth transfers among stakeholders. More importantly, acting on behalf of their shareholders or pursuing their self-interests, managers of both targets and acquirers may be equally motivated to manipulate earnings prior to an acquisition to generate higher gains for their shareholders or themselves. Building on the grounds of information asymmetry, agency conflicts, stewardship theory, and the revelation principle, this study addresses the question of whether takeover targets employ accrual and real earnings management in the periods prior to the announcement of Mergers and Acquisitions (M&A). Additionally, this study examines whether acquirers are able to detect targets’ earnings management, and in response, adjust the acquisition premium paid in order not to face the risk of overpayment. This study uses an aggregate accruals approach in estimating accrual earnings management as proxied by estimated abnormal accruals. Additionally, real earnings management is proxied for by employing widely used models in accounting and finance literature. The results of this study indicate that takeover targets manipulate their earnings using accruals in the second year with an earnings release prior to the announcement of the M&A. Moreover, in partitioning the sample of targets according to the method of payment used in the deal, the results are restricted only to targets of stock-financed deals. These results are consistent with the argument that targets of cash-only or mixed-payment deals do not have the same strong motivations to manage their earnings as their stock-financed deals counterparts do additionally supporting the findings of prior studies that the method of payment in takeovers is value relevant. The findings of this study also indicate that takeover targets manipulate earnings upwards through cutting discretionary expenses the year prior to the acquisition while they do not do so by manipulating sales or production costs. Moreover, in partitioning the sample of targets according to the method of payment used in the deal, the results are restricted only to targets of stock-financed deals, providing further robustness to the results derived under the accrual-based models. Finally, this study finds evidence suggesting that acquirers are fully aware of the accrual-based techniques employed by takeover targets and can unveil such manipulation practices. These results are robust to alternative accrual and real earnings management proxies, as well as controlling for the method of payment in the deal.

Keywords: accrual earnings management, acquisition premium, real earnings management, takeover targets

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Authors: Pichaya Poonlarp, Danai Boonyakiat, C. Chuamuangphan, M. Chanta

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Current consumers’ behavior trends have changed towards more health awareness, the well-being of society and interest of nature and environment. The Royal Project Foundation is, therefore, well aware of organic agriculture. The project only focused on using natural products and utilizing its highland biological merits to increase resistance to diseases and insects for the produce grown. The project also brought in basic knowledge from a variety of available research information, including, but not limited to, improvement of soil fertility and a control of plant insects with biological methods in order to lay a foundation in developing and promoting farmers to grow quality produce with a high health safety. This will finally lead to sustainability for future highland agriculture and a decrease of chemical use on the highland area which is a source of natural watershed. However, there are still shortcomings of the postharvest management in term of quality and losses, such as bruising, rottenness, wilting and yellowish leaves. These losses negatively affect the maintenance and a shelf life of organic vegetables. Therefore, it is important that a research study of the appropriate and effective postharvest management is conducted for an individual organic vegetable to minimize product loss and find root causes of postharvest losses which would contribute to future postharvest management best practices. This can be achieved through surveys and data collection from postharvest processes in order to conduct analysis for causes of postharvest losses of organic pak-choi, baby pak-choi, and choy sum. Consequently, postharvest losses reduction strategies of organic vegetables can be achieved. In this study, postharvest losses of organic pak choi, baby pak-choi, and choy sum were determined at each stage of the supply chain starting from the field after harvesting, at the Development Center packinghouse, at Chiang Mai packinghouse, at Bangkok packing house and at the Royal Project retail shop in Chiang Mai. The results showed that postharvest losses of organic pak-choi, baby pak-choi, and choy sum were 86.05, 89.05 and 59.03 percent, respectively. The main factors contributing to losses of organic vegetables were due to mechanical damage and underutilized parts and/or short of minimum quality standard. Good practices had been developed after causes of losses were identified. Appropriate postharvest handling and management, for example, temperature control, hygienic cleaning, and reducing the duration of the supply chain, postharvest losses of all organic vegetables should be able to remarkably reduced postharvest losses in the supply chain.

Keywords: postharvest losses, organic vegetables, handling improvement, shelf life, supply chain

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Authors: Julio Manuel de Luis Ruiz, Javier Sedano Cibrián, Rubén Pérez Álvarez, Raúl Pereda García, Cristina Diego Soroa

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Nowadays, the use of drones has been extended to practically any human activity. One of the main applications is focused on the surveying field. In this regard, software programs that process the images captured by the sensor from the drone in an almost automatic way have been developed and commercialized, but they only allow contrasting the results through control points. This work proposes the contrast of a 3D model obtained from a flight developed by a drone and a non-metric camera (due to its low cost), with a second model that is obtained by means of the historically-endorsed classical methods. In addition to this, the contrast is developed over a certain territory with a significant unevenness, so as to test the model generated with photogrammetry, and considering that photogrammetry with drones finds more difficulties in terms of accuracy in this kind of situations. Distances, heights, surfaces and volumes are measured on the basis of the 3D models generated, and the results are contrasted. The differences are about 0.2% for the measurement of distances and heights, 0.3% for surfaces and 0.6% when measuring volumes. Although they are not important, they do not meet the order of magnitude that is presented by salespeople.

Keywords: accuracy, classical topographic, model tridimensional, photogrammetry, Uav.

Procedia PDF Downloads 141

Authors: Obe Olumide Olayinka, Victor Balanica, Eugen Neagoe

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The effects of hypertension are often lethal thus its early detection and prevention is very important for everybody. In this paper, a neural network (NN) model was developed and trained based on a dataset of hypertension causative parameters in order to forecast the likelihood of occurrence of hypertension in patients. Our research goal was to analyze the potential of the presented NN to predict, for a period of time, the risk of hypertension or the risk of developing this disease for patients that are or not currently hypertensive. The results of the analysis for a given patient can support doctors in taking pro-active measures for averting the occurrence of hypertension such as recommendations regarding the patient behavior in order to lower his hypertension risk. Moreover, the paper envisages a set of three example scenarios in order to determine the age when the patient becomes hypertensive, i.e. determine the threshold for hypertensive age, to analyze what happens if the threshold hypertensive age is set to a certain age and the weight of the patient if being varied, and, to set the ideal weight for the patient and analyze what happens with the threshold of hypertensive age.

Keywords: neural network, hypertension, data set, training set, supervised learning

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Authors: Helmi Temimi

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In this paper, we study the super-convergence properties of the discontinuous Galerkin (DG) method applied to one-dimensional mth-order ordinary differential equations without introducing auxiliary variables. We found that nth−derivative of the DG solution exhibits an optimal O (hp+1−n) convergence rates in the L2-norm when p-degree piecewise polynomials with p≥1 are used. We further found that the odd-derivatives and the even derivatives are super convergent, respectively, at the upwind and downwind endpoints.

Keywords: discontinuous, galerkin, superconvergence, higherorder, error, estimates

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Authors: A. A. Okafor, C. H. Achebe, J. L. Chukwuneke, C. G. Ozoegwu

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The performance of an engine whose basic design parameters are known can be predicted with the assistance of simulation programs into the less time, cost and near value of actual. This paper presents a comprehensive mathematical model of the performance parameters of four stroke spark ignition engine. The essence of this research work is to develop a mathematical model for the analysis of engine performance parameters of four stroke spark ignition engine before embarking on full scale construction, this will ensure that only optimal parameters are in the design and development of an engine and also allow to check and develop the design of the engine and it’s operation alternatives in an inexpensive way and less time, instead of using experimental method which requires costly research test beds. To achieve this, equations were derived which describe the performance parameters (sfc, thermal efficiency, mep and A/F). The equations were used to simulate and optimize the engine performance of the model for various engine speeds. The optimal values obtained for the developed bivariate mathematical models are: sfc is 0.2833kg/kwh, efficiency is 28.77% and a/f is 20.75.

Keywords: bivariate models, engine performance, injector engine, optimization, performance parameters, simulation, spark ignition

Procedia PDF Downloads 333

Authors: Yiğit Gürler, İbrahim Şimşek, Müge Savaştaer, Ayberk Karakuş, Alper Taşdemirci

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316L steel is frequently used in the industry due to its easy formability and accessibility in sheet metal forming processes. Numerical and experimental studies are frequently encountered in the literature to examine the mechanical behavior of 316L stainless steel during the forming process. 316L stainless steel is the most common material used in the production of plate heat exchangers and plate heat exchangers are produced by plastic deformation of the stainless steel. The motivation in this study is to determine the appropriate material model during the simulation of the sheet metal forming process. For this reason, two different material models were examined and Ls-Dyna material cards were created using material test data. These are MAT133_BARLAT_YLD2000 and MAT093_SIMPLIFIED_JOHNSON_COOK. In order to compare results of the tensile test & hydraulic bulge test performed both numerically and experimentally. The obtained results were evaluated comparatively and the most suitable material model was selected for the forming simulation. In future studies, this material model will be used in the numerical modeling of the sheet metal forming process.

Keywords: 316L, mechanical characterization, metal forming, Ls-Dyna

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Authors: Ezeoke Callistus Obiora, Ezeoke Nneka Angela

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The study investigated the effect of deficit financing on macroeconomic variables in Nigeria. The specific objectives included to find out the relationship between deficit financing and GDP, interest rate, inflation rate, money supply, exchange rate and private investment respectively on a time series covering a period of 44 years (1970 – 2013). The Ordinary Least Square multiple regression produced statistics for the coefficient of determination (R2), F-test, t-test used for the interpretation of the study. The findings revealed that Deficit financing has significant positive effect on GDP and exchange rate. Again, deficit financing has a positive and insignificant relationship inflation, money supply and investment. Only interest rate recorded negative yet insignificant relationship with deficit financing. The implications of the findings are that deficit financing can be a veritable tool for boosting economic development in Nigeria, but the influential positively rising exchange rate implies that deficit financing devalues the Naira exchange rate to other currencies indicating that deficit financing can affect Nigerians competitive advantage at the world market. Thus, the study concludes that deficit financing has not encouraged economic growth in Nigeria.

Keywords: deficit financing, money supply, exchange rate, inflation, GDP, investment, Nigeria

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Authors: Yeo Kyeong Lee, Hae Won Min, Ji Yeon Kang, Hee Sun Kim, Yeong Soo Shin

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Fire incidents have been steadily increased over the last year according to national emergency management agency of South Korea. Even though most of the fire incidents with property damage have been occurred in building, rehabilitation has not been properly done with consideration of structure safety. Therefore, this study aims at evaluating rehabilitation effects on fire damaged normal strength concrete beams through experiments and finite element analyses. For the experiments, reinforced concrete beams were fabricated having designed concrete strength of 21 MPa. Two different cover thicknesses were used as 40 mm and 50 mm. After cured, the fabricated beams were heated for 1hour or 2hours according to ISO-834 standard time-temperature curve. Rehabilitation was done by removing the damaged part of cover thickness and filling polymeric mortar into the removed part. Both fire damaged beams and rehabilitated beams were tested with four point loading system to observe structural behaviors and the rehabilitation effect. To verify the experiment, finite element (FE) models for structural analysis were generated using commercial software ABAQUS 6.10-3. For the rehabilitated beam models, integrated temperature-structural analyses were performed in advance to obtain geometries of the fire damaged beams. In addition to the fire damaged beam models, rehabilitated part was added with material properties of polymeric mortar. Three dimensional continuum brick elements were used for both temperature and structural analyses. The same loading and boundary conditions as experiments were implemented to the rehabilitated beam models and non-linear geometrical analyses were performed. Test results showed that maximum loads of the rehabilitated beams were 8~10% higher than those of the non-rehabilitated beams and even 1~6 % higher than those of the non-fire damaged beam. Stiffness of the rehabilitated beams were also larger than that of non-rehabilitated beams but smaller than that of the non-fire damaged beams. In addition, predicted structural behaviors from the analyses also showed good rehabilitation effect and the predicted load-deflection curves were similar to the experimental results. From this study, both experiments and analytical results demonstrated good rehabilitation effect on the fire damaged normal strength concrete beams. For the further, the proposed analytical method can be used to predict structural behaviors of rehabilitated and fire damaged concrete beams accurately without suffering from time and cost consuming experimental process.

Keywords: fire, normal strength concrete, rehabilitation, reinforced concrete beam

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Authors: Ammarah Irum, Muhammad Ali Tahir

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Document-level sentiment analysis in Urdu is a challenging Natural Language Processing (NLP) task due to the difficulty of working with lengthy texts in a language with constrained resources. Deep learning models, which are complex neural network architectures, are well-suited to text-based applications in addition to data formats like audio, image, and video. To investigate the potential of deep learning for Urdu sentiment analysis, we implemented five different deep learning models, including Bidirectional Long Short Term Memory (BiLSTM), Convolutional Neural Network (CNN), Convolutional Neural Network with Bidirectional Long Short Term Memory (CNN-BiLSTM), and Bidirectional Encoder Representation from Transformer (BERT). In this study, we developed a hybrid deep learning model called BiLSTM-Single Layer Multi Filter Convolutional Neural Network (BiLSTM-SLMFCNN) by fusing BiLSTM and CNN architecture. The proposed and baseline techniques are applied on Urdu Customer Support data set and IMDB Urdu movie review data set by using pre-trained Urdu word embedding that are suitable for sentiment analysis at the document level. Results of these techniques are evaluated and our proposed model outperforms all other deep learning techniques for Urdu sentiment analysis. BiLSTM-SLMFCNN outperformed the baseline deep learning models and achieved 83%, 79%, 83% and 94% accuracy on small, medium and large sized IMDB Urdu movie review data set and Urdu Customer Support data set respectively.

Keywords: urdu sentiment analysis, deep learning, natural language processing, opinion mining, low-resource language

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Authors: Belkacem Chebil Sonia, Bensalem Wacef

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The majority of ball end milling models includes only the influence of cutting parameters (cutting speed, feed rate, depth of cut). Furthermore, this influence is studied in most of works on cutting force. Therefore, this study proposes an accurate ball end milling process modeling which includes also the influence of tool workpiece inclination. In addition, a characterization of residual stresses resulting of thermo mechanical loading in the workpiece was also presented. Moreover, the study of the influence of tool workpiece inclination and cutting parameters was made on residual stresses distribution. In order to achieve the predetermination of cutting forces and residual stresses during a milling operation, a thermo mechanical three-dimensional numerical model of ball end milling was developed. Furthermore, an experimental companion of ball end milling tests was realized on a 5-axis machining center to determine the cutting forces and characterize the residual stresses. The simulation results are compared with the experiment to validate the Finite Element Model and subsequently identify the optimum inclination angle and cutting parameters.

Keywords: ball end milling, cutting forces, cutting parameters, residual stress, tool-workpiece inclination

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Authors: Fahmi Hassan, Noorizan Abdul Aziz, Yahaya Hassan, Hazlinda Abu Hassan

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Sepsis incidence in critical care settings is a major problem in health care. Extended antibiotic infusion is thought to be superior to traditional dosing especially when treating critically ill patients with sepsis. We compared clinical outcomes of critically ill patients with sepsis receiving 30-minute meropenem infusion and three-hour meropenem infusion. A retrospective case-control study was conducted among septic patients treated with meropenem infusion in ICUs of three hospitals. Patients included in the study received either extended or standard meropenem infusion as per the practice of individual settings. Outcomes and clinical data were retrospectively collected from the electronic databases and patients’ files. A total of 108 patients received extended meropenem infusion while another 117 patients received standard meropenem infusion. Patients receiving the extended meropenem infusion were found to have a significantly lower shorter length of hospital and ICU stay. It was also found that among those receiving extended meropenem infusion, 54.7% (64/117) had a reduction of SAPS II score, while only 44% (48/108) of patients receiving standard meropenem infusion had reduced scores. This study will strengthen the evidence in using extended meropenem infusion as a standard practice in critical care settings. As this is the first study of its kind done in Malaysia, it proves that prolonged meropenem infusion may be beneficial to critically ill patients with sepsis. However, randomized clinical trials with large sample size should be carried out in local settings in order to minimize other confounders that may influence with the result of the study.

Keywords: antibiotics, beta lactams, critical care, extended infusion, meropenem

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Authors: Hermann Kuehnle

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Developments in ICT totally reshape manufacturing as machines, objects and equipment on the shop floors will be smart and online. Interactions with virtualizations and models of a manufacturing unit will appear exactly as interactions with the unit itself. These virtualizations may be driven by providers with novel ICT services on demand that might jeopardize even well established business models. Context aware equipment, autonomous orders, scalable machine capacity or networkable manufacturing unit will be the terminology to get familiar with in manufacturing and manufacturing management. Such newly appearing smart abilities with impact on network behavior, collaboration procedures and human resource development will make distributed manufacturing a preferred model to produce. Computing miniaturization and smart devices revolutionize manufacturing set ups, as virtualizations and atomization of resources unwrap novel manufacturing principles. Processes and resources obey novel specific laws and have strategic impact on manufacturing and major operational implications. Mechanisms from distributed manufacturing engaging interacting smart manufacturing units and decentralized planning and decision procedures already demonstrate important effects from this shift of focus towards collaboration and interoperability.

Keywords: autonomous unit, networkability, smart manufacturing unit, virtualization

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Authors: Minh Dong Le, Viet Dung Nguyen, Do Huu Viet, Nguyen Huu Tu

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In this paper, we present a new method for circumscribed masses detection in mammograms. Our method is evaluated on 23 mammographic images of circumscribed masses and 20 normal mammograms from public Mini-MIAS database. The method is quite sanguine with sensitivity (SE) of 95% with only about 1 false positive per image (FPpI). To achieve above results we carry out a progression following: Firstly, the input images are preprocessed with the aim to enhance key information of circumscribed masses; Next, we calculate and evaluate statistically basic features of abnormal regions on training database; Then, mammograms on testing database are divided into equal blocks which calculated corresponding features. Finally, using priority order of basic features to classify blocks as an abnormal or normal regions.

Keywords: mammograms, circumscribed masses, evaluated statistically, priority order of basic features

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Authors: Hayder H. Kareem, Shunqi Pan

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The geological environment where the groundwater is collected represents the most important element that affects the behaviour of groundwater aquifer. As groundwater is a worldwide vital resource, it requires knowing the parameters that affect this source accurately so that the conceptualized mathematical models would be acceptable to the broadest ranges. Therefore, groundwater models have recently become an effective and efficient tool to investigate groundwater aquifer behaviours. Groundwater aquifer may contain aquitards, aquicludes, or interfaces within its geological formations. Aquitards and aquicludes have geological formations that forced the modellers to include those formations within the conceptualized groundwater models, while interfaces are commonly neglected from the conceptualization process because the modellers believe that the interface has no effect on aquifer behaviour. The current research highlights the impact of an interface existing in a real unconfined groundwater aquifer called Dibdibba, located in Al-Najaf City, Iraq where it has a river called the Euphrates River that passes through the eastern part of this city. Dibdibba groundwater aquifer consists of two types of soil layers separated by an interface soil layer. A groundwater model is built for Al-Najaf City to explore the impact of this interface. Calibration process is done using PEST 'Parameter ESTimation' approach and the best Dibdibba groundwater model is obtained. When the soil interface is conceptualized, results show that the groundwater tables are significantly affected by that interface through appearing dry areas of 56.24 km² and 6.16 km² in the upper and lower layers of the aquifer, respectively. The Euphrates River will also leak water into the groundwater aquifer of 7359 m³/day. While these results are changed when the soil interface is neglected where the dry area became 0.16 km², the Euphrates River leakage became 6334 m³/day. In addition, the conceptualized models (with and without interface) reveal different responses for the change in the recharge rates applied on the aquifer through the uncertainty analysis test. The aquifer of Dibdibba in Al-Najaf City shows a slight deficit in the amount of water supplied by the current pumping scheme and also notices that the Euphrates River suffers from stresses applied to the aquifer. Ultimately, this study shows a crucial need to represent the interface soil layer in model conceptualization to be the intended and future predicted behaviours more reliable for consideration purposes.

Keywords: Al-Najaf City, groundwater aquifer behaviour, groundwater modelling, interface soil layer, Visual MODFLOW

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Authors: S. Jalilzadeh, S. M. Mohseni Bonab

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Solar and battery energy storage systems are very useful for consumers who live in deprived areas and do not have access to electricity distribution networks. Nowadays one of the problems that photo voltaic systems (PV) have changing of output power in temperature and irradiance variations, which directly affects the load that is connected to photo voltaic systems. In this paper, with considering the fact that the solar array varies with change in temperature and solar power radiation, a voltage stabilizer system of a load connected to photo voltaic array is designed to stabilize the load voltage and to transfer surplus power of the battery. Also, in proposed hybrid system, the needed load power amount is supplemented considering the voltage stabilization in standalone operation for supplying unbalanced AC load. Electrical energy storage system for voltage control and improvement of the performance of PV by a DC/DC converter is connected to the DC bus. The load is also feed by an AC/DC converter. In this paper, when the voltage increases in its reference limit, the battery gets charged by the photo voltaic array and when it decreases in its defined limit, the power gets injected to the DC bus by this battery. The constant of DC bus Voltage is the cause for the reduced harmonics generated by the inverter. In addition, a series of filters are provided in the inverter output in to reduced harmonics. The inverter control circuit is designed that the voltage and frequency of the load remain almost constant at different load conditions. This paper has focused on controlling strategies of converters to improve their performance.

Keywords: photovoltaic array (PV), DC/DC Boost converter, battery converter, inverters control

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Authors: Irene Yi

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Machine learning, natural language processing, and neural network models of language are becoming more and more prevalent in the fields of technology and linguistics today. Training data for machines are at best, large corpora of human literature and at worst, a reflection of the ugliness in society. Machines have been trained on millions of human books, only to find that in the course of human history, derogatory and sexist adjectives are used significantly more frequently when describing females in history and literature than when describing males. This is extremely problematic, both as training data, and as the outcome of natural language processing. As machines start to handle more responsibilities, it is crucial to ensure that they do not take with them historical sexist and misogynistic notions. This paper gathers data and algorithms from neural network models of language having to deal with syntax, semantics, sociolinguistics, and text classification. Results are significant in showing the existing intentional and unintentional misogynistic notions used to train machines, as well as in developing better technologies that take into account the semantics and syntax of text to be more mindful and reflect gender equality. Further, this paper deals with the idea of non-binary gender pronouns and how machines can process these pronouns correctly, given its semantic and syntactic context. This paper also delves into the implications of gendered grammar and its effect, cross-linguistically, on natural language processing. Languages such as French or Spanish not only have rigid gendered grammar rules, but also historically patriarchal societies. The progression of society comes hand in hand with not only its language, but how machines process those natural languages. These ideas are all extremely vital to the development of natural language models in technology, and they must be taken into account immediately.

Keywords: gendered grammar, misogynistic language, natural language processing, neural networks

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Authors: Lilla Dorina Habsz, Marta Rado

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Popularity is affected by a variety of factors in the primary school such as academic achievement and ethnicity. The main goal of our study was to analyse whether acting white exists in Hungarian elementary schools. In other words, we observed whether Roma students penalize those in-group members who obtain the high academic achievement. Furthermore, to show how popularity is influenced by changes in academic achievement in inter-ethnic relations. The empirical basis of our research was the 'competition and negative networks' longitudinal dataset, which was collected by the MTA TK 'Lendület' RECENS research group. This research followed 11 and 12-year old students for a two-year period. The survey was analysed using fixed and random effect models. Overall, we found a positive correlation between grades and popularity, but no evidence for the acting white effect. However, better grades were more positively evaluated within the majority group than within the minority group, which may further increase inequalities.

Keywords: academic achievement, elementary school, ethnicity, popularity

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Authors: David F. Nettleton, Elodie Bugnicourt, Christian Wasiak, Alejandro Rosales

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In this presentation, preliminary results are given for the modeling and calibration of two different industrial winding MIMO (Multiple Input Multiple Output) processes using machine learning techniques. In contrast to previous approaches which have typically used ‘black-box’ linear statistical methods together with a definition of the mechanical behavior of the process, we use non-linear machine learning algorithms together with a ‘white-box’ rule induction technique to create a supervised model of the fitting error between the expected and real force measures. The final objective is to build a precise model of the winding process in order to control de-tension of the material being wound in the first case, and the friction of the material passing through the die, in the second case. Case 1, Tension Control of a Winding Process. A plastic web is unwound from a first reel, goes over a traction reel and is rewound on a third reel. The objectives are: (i) to train a model to predict the web tension and (ii) calibration to find the input values which result in a given tension. Case 2, Friction Force Control of a Micro-Pullwinding Process. A core+resin passes through a first die, then two winding units wind an outer layer around the core, and a final pass through a second die. The objectives are: (i) to train a model to predict the friction on die2; (ii) calibration to find the input values which result in a given friction on die2. Different machine learning approaches are tested to build models, Kernel Ridge Regression, Support Vector Regression (with a Radial Basis Function Kernel) and MPART (Rule Induction with continuous value as output). As a previous step, the MPART rule induction algorithm was used to build an explicative model of the error (the difference between expected and real friction on die2). The modeling of the error behavior using explicative rules is used to help improve the overall process model. Once the models are built, the inputs are calibrated by generating Gaussian random numbers for each input (taking into account its mean and standard deviation) and comparing the output to a target (desired) output until a closest fit is found. The results of empirical testing show that a high precision is obtained for the trained models and for the calibration process. The learning step is the slowest part of the process (max. 5 minutes for this data), but this can be done offline just once. The calibration step is much faster and in under one minute obtained a precision error of less than 1x10-3 for both outputs. To summarize, in the present work two processes have been modeled and calibrated. A fast processing time and high precision has been achieved, which can be further improved by using heuristics to guide the Gaussian calibration. Error behavior has been modeled to help improve the overall process understanding. This has relevance for the quick optimal set up of many different industrial processes which use a pull-winding type process to manufacture fibre reinforced plastic parts. Acknowledgements to the Openmind project which is funded by Horizon 2020 European Union funding for Research & Innovation, Grant Agreement number 680820

Keywords: data model, machine learning, industrial winding, calibration

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Authors: Alireza Fallahfard, Ludwig Vinches, Stephane Halle

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In the workplace, the exposure level of airborne contaminants should be evaluated due to health and safety issues. It can be done by numerical models or experimental measurements, but the numerical approach can be useful when it is challenging to perform experiments. One of the simplest models is the well-mixed room (WMR) model, which has shown its usefulness to predict inhalation exposure in many situations. However, since the WMR is limited to gases and vapors, it cannot be used to predict exposure to aerosols. The main objective is to modify the WMR model to expand its application to exposure scenarios involving aerosols. To reach this objective, the standard WMR model has been modified to consider the deposition of particles by gravitational settling and Brownian and turbulent deposition. Three deposition models were implemented in the model. The time-dependent concentrations of airborne particles predicted by the model were compared to experimental results conducted in a 0.512 m3 chamber. Polystyrene particles of 1, 2, and 3 µm in aerodynamic diameter were generated with a nebulizer under two air changes per hour (ACH). The well-mixed condition and chamber ACH were determined by the tracer gas decay method. The mean friction velocity on the chamber surfaces as one of the input variables for the deposition models was determined by computational fluid dynamics (CFD) simulation. For the experimental procedure, the particles were generated until reaching the steady-state condition (emission period). Then generation stopped, and concentration measurements continued until reaching the background concentration (decay period). The results of the tracer gas decay tests revealed that the ACHs of the chamber were: 1.4 and 3.0, and the well-mixed condition was achieved. The CFD results showed the average mean friction velocity and their standard deviations for the lowest and highest ACH were (8.87 ± 0.36) ×10-2 m/s and (8.88 ± 0.38) ×10-2 m/s, respectively. The numerical results indicated the difference between the predicted deposition rates by the three deposition models was less than 2%. The experimental and numerical aerosol concentrations were compared in the emission period and decay period. In both periods, the prediction accuracy of the modified model improved in comparison with the classic WMR model. However, there is still a difference between the actual value and the predicted value. In the emission period, the modified WMR results closely follow the experimental data. However, the model significantly overestimates the experimental results during the decay period. This finding is mainly due to an underestimation of the deposition rate in the model and uncertainty related to measurement devices and particle size distribution. Comparing the experimental and numerical deposition rates revealed that the actual particle deposition rate is significant, but the deposition mechanisms considered in the model were ten times lower than the experimental value. Thus, particle deposition was significant and will affect the airborne concentration in occupational settings, and it should be considered in the airborne exposure prediction model. The role of other removal mechanisms should be investigated.

Keywords: aerosol, CFD, exposure assessment, occupational settings, well-mixed room model, zonal model

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Authors: Hossein Tavallali, Mohammad Ali Karimi, Gohar Deilamy-Rad

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The proposed method for speciation, preconcentration and determination of Fe(II) and Fe(III) in pharmaceutical products was developed using of alumina-coated magnetite nanoparticles (Fe₃O₄/Al₂O₃ NPs) as solid phase extraction (SPE) sorbent in magnetic mixed hemimicell solid phase extraction (MMHSPE) technique followed by flame atomic absorption spectrometry analysis. The procedure is based on complexation of Fe(II) with 1, 10-phenanthroline (OP) as complexing reagent for Fe(II) that immobilized on the modified Fe₃O₄/Al₂O₃ NPs. The extraction and concentration process for pharmaceutical sample was carried out in a single step by mixing the extraction solvent, magnetic adsorbents under ultrasonic action. Then, the adsorbents were isolated from the complicated matrix easily with an external magnetic field. Fe(III) ions determined after facility reduced to Fe(II) by added a proper reduction agent to sample solutions. Compared with traditional methods, the MMHSPE method simplified the operation procedure and reduced the analysis time. Various influencing parameters on the speciation and preconcentration of trace iron, such as pH, sample volume, amount of sorbent, type and concentration of eluent, were studied. Under the optimized operating conditions, the preconcentration factor of the modified nano magnetite for Fe(II) 167 sample was obtained. The detection limits and linear range of this method for iron were 1.0 and 9.0 - 175 ng.mL⁻¹, respectively. Also the relative standard deviation for five replicate determinations of 30.00 ng.mL^-1 Fe²⁺ was 2.3%.

Keywords: Alumina-Coated magnetite nanoparticles, Magnetic Mixed Hemimicell Solid-Phase Extraction, Fe(ΙΙ) and Fe(ΙΙΙ), pharmaceutical sample

Procedia PDF Downloads 297

Authors: Omkar More, Isha Saini, Gracy Mathai

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To study whether there is any clinical correlation between pterygium and dry eye and to evaluate the status of the tear film in patients with pterygium. Methods: 100 eyes with pterygium were compared with 100 control eyes without pterygium. Patients between 20 – 70 years were included in the study. A detailed history was taken and Schirmer’s test and TBUT were performed on all to evaluate the status of dry eye. Schirmer’s test ˂ 10 mm and TBUT ˂10 seconds was considered abnormal. Results: Maximum number (52) of patients affected by dry eye in both the groups were in the age group 31-40 years which statistically showed age as a significant factor of association for both pterygium and dry eye (P < 0.01).Schirmer’s test was slightly reduced in patients with pterygium(18.73±5.69 mm). TBUT was significantly reduced in the case group (12.26±2.24sec).TBUT decreased maximally in 51-60 yrs age group (13.00±2.77sec) with pterygium showing a tear film instability. On comparison of pterygia and controls with normal and abnormal tear film, Odd’s Ratio was 1.14 showing a risk of dry eye in pterygia patients to be 1.14 times higher than controls. Conclusion: Whether tear dysfunction is a precursor to pterygium growth or pterygium causes tear dysfunction is still not clear. Research and clinical evidence, however, suggest that there is a relationship between the two. This study is, therefore, undertaken to investigate the correlation between pterygium and dry eye. The patients with pterygia were compared with normals to evaluate their status regarding dryness. A close relationship exists between ocular irritation symptoms and functional evidence of tear instability. Schirmer’s test and TBUT should routinely be used in the outpatient department to diagnose dry eye in patients with pterygium and these patients should be promptly treated to prevent any sight-threatening complications.

Keywords: footfall, nursing assessment, quality improvement, six sigma

Procedia PDF Downloads 361